




'''simple docstring'''



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCamelCase_									=		16
lowerCamelCase_									=		32
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Optional[Any]			,	__A							:					List[Any] = 16    )					->					Any:
   _SCREAMING_SNAKE_CASE			= AutoTokenizer.from_pretrained("bert-base-cased"    )
   _SCREAMING_SNAKE_CASE			= load_dataset("glue"			,	"mrpc"    )

   def tokenize_function(__A							:					Optional[Any]    ):
      # max_length=None => use the model max length (it's actually the default)
      _SCREAMING_SNAKE_CASE			= tokenizer(examples["sentence1"]			,	examples["sentence2"]			,	truncation=snake_case__			,	max_length=snake_case__    )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      _SCREAMING_SNAKE_CASE			= datasets.map(
          snake_case__			,	batched=snake_case__			,	remove_columns=["idx", "sentence1", "sentence2"]			,	)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   _SCREAMING_SNAKE_CASE			= tokenized_datasets.rename_column("label"			,	"labels"    )

   def collate_fn(__A							:					str    ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _SCREAMING_SNAKE_CASE			= 1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _SCREAMING_SNAKE_CASE			= 16
      elif accelerator.mixed_precision != "no":
         _SCREAMING_SNAKE_CASE			= 8
      else:
         _SCREAMING_SNAKE_CASE			= None

      return tokenizer.pad(
          snake_case__			,	padding="longest"			,	max_length=snake_case__			,	pad_to_multiple_of=snake_case__			,	return_tensors="pt"			,	)

   # Instantiate dataloaders.
   _SCREAMING_SNAKE_CASE			= DataLoader(
       tokenized_datasets["train"]			,	shuffle=snake_case__			,	collate_fn=snake_case__			,	batch_size=snake_case__    )
   _SCREAMING_SNAKE_CASE			= DataLoader(
       tokenized_datasets["validation"]			,	shuffle=snake_case__			,	collate_fn=snake_case__			,	batch_size=snake_case__    )

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 lowerCamelCase_									=		mocked_dataloaders  # noqa: F811
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Any			,	__A							:					Tuple    )					->					str:
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"			,	snake_case__    ) == "1":
      _SCREAMING_SNAKE_CASE			= 2
   # Initialize accelerator
   _SCREAMING_SNAKE_CASE			= Accelerator(cpu=args.cpu			,	mixed_precision=args.mixed_precision    )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _SCREAMING_SNAKE_CASE			= config["lr"]
   _SCREAMING_SNAKE_CASE			= int(config["num_epochs"]    )
   _SCREAMING_SNAKE_CASE			= int(config["seed"]    )
   _SCREAMING_SNAKE_CASE			= int(config["batch_size"]    )

   _SCREAMING_SNAKE_CASE			= evaluate.load("glue"			,	"mrpc"    )

   # If the batch size is too big we use gradient accumulation
   _SCREAMING_SNAKE_CASE			= 1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      _SCREAMING_SNAKE_CASE			= batch_size // MAX_GPU_BATCH_SIZE
      _SCREAMING_SNAKE_CASE			= MAX_GPU_BATCH_SIZE

   set_seed(snake_case__    )
   _SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE			= get_dataloaders(snake_case__			,	snake_case__    )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _SCREAMING_SNAKE_CASE			= AutoModelForSequenceClassification.from_pretrained("bert-base-cased"			,	return_dict=snake_case__    )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   _SCREAMING_SNAKE_CASE			= model.to(accelerator.device    )

   # Instantiate optimizer
   _SCREAMING_SNAKE_CASE			= AdamW(params=model.parameters()			,	lr=snake_case__    )

   # Instantiate scheduler
   _SCREAMING_SNAKE_CASE			= get_linear_schedule_with_warmup(
       optimizer=snake_case__			,	num_warmup_steps=1_00			,	num_training_steps=(len(snake_case__    ) * num_epochs) // gradient_accumulation_steps			,	)

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   _SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE			= accelerator.prepare(
       snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__    )

   # Now we train the model
   for epoch in range(snake_case__    ):
      model.train()
      for step, batch in enumerate(snake_case__    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device    )
         _SCREAMING_SNAKE_CASE			= model(**snake_case__    )
         _SCREAMING_SNAKE_CASE			= outputs.loss
         _SCREAMING_SNAKE_CASE			= loss / gradient_accumulation_steps
         accelerator.backward(snake_case__    )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      _SCREAMING_SNAKE_CASE			= 0
      for step, batch in enumerate(snake_case__    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device    )
         with torch.no_grad():
            _SCREAMING_SNAKE_CASE			= model(**snake_case__    )
         _SCREAMING_SNAKE_CASE			= outputs.logits.argmax(dim=-1    )
         _SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE			= accelerator.gather((predictions, batch["labels"])    )
         # New Code #
         # First we check if it's a distributed system
         if accelerator.use_distributed:
            # Then see if we're on the last batch of our eval dataloader
            if step == len(snake_case__    ) - 1:
               # Last batch needs to be truncated on distributed systems as it contains additional samples
               _SCREAMING_SNAKE_CASE			= predictions[: len(eval_dataloader.dataset    ) - samples_seen]
               _SCREAMING_SNAKE_CASE			= references[: len(eval_dataloader.dataset    ) - samples_seen]
            else:
               # Otherwise we add the number of samples seen
               samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
         metric.add_batch(
             predictions=snake_case__			,	references=snake_case__			,	)

      _SCREAMING_SNAKE_CASE			= metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"""epoch {epoch}:"""			,	snake_case__    )
def 					SCREAMING_SNAKE_CASE_					(				)					->					Union[str, Any]:
   _SCREAMING_SNAKE_CASE			= argparse.ArgumentParser(description="Simple example of training script."    )
   parser.add_argument(
       "--mixed_precision"			,	type=snake_case__			,	default=snake_case__			,	choices=["no", "fp16", "bf16", "fp8"]			,	help="Whether to use mixed precision. Choose"
       "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
       "and an Nvidia Ampere GPU."			,	)
   parser.add_argument("--cpu"			,	action="store_true"			,	help="If passed, will train on the CPU."    )
   _SCREAMING_SNAKE_CASE			= parser.parse_args()
   _SCREAMING_SNAKE_CASE			= {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(snake_case__			,	snake_case__    )


if __name__ == "__main__":
 main()







"""simple docstring"""




import warnings

from .generation import TFGenerationMixin






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				# warning at import time
				warnings.warn(
				    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
				    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,   UpperCAmelCase__		,   )



